Cyto-Seq

CytoSeq

CytoSeq enables gene expression profiling of thousands of single cells. In this method, single cells are randomly deposited into wells. A combinatorial library of beads with specific capture probes is added to each well. After cell lysis, mRNAs hybridize the to beads, which are pooled subsequently for RT, amplification, and sequencing. Deep sequencing provides accurate, high-coverage gene expression profiles of several single cells.

Similar methods: CEL-Seq, Quartz-Seq, MARS-Seq, inDrop, Hi-SCL.

Pros:

• Can readily scale to 10,000s or 100,000s of cells
• Complements and expands the capabilities of fluorescence or mass spectrometry–based cytometry
• Detects any transcribed mRNA without the limitations of antibody availability
• Enables rare cell characterization on small samples with insufficient cells for traditional flow cytometry
• Allows direct analysis of complex samples of heterogeneous cell size and shape

Cons:

• Sequencing depth requires large number of reads (eg, 200,000 transcripts per cell requires 2 million reads for 10X coverage: 2 billion reads for 1000 cells)
• Single run can be relatively expensive and time-consuming
• Trade-off between depth of sequencing and differential gene expression

• CytoSeq: Fan H. C., Fu G. K. and Fodor S. P. (2015) Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347: 1258367

1. Friedensohn S., Khan T. A. and Reddy S. T. Advanced Methodologies in High-Throughput Sequencing of Immune Repertoires. Trends in Biotechnology. 2017;35:203-214
2. Kolodziejczyk A. A., Kim J. K., Svensson V., Marioni J. C. and Teichmann S. A. The Technology and Biology of Single-Cell RNA Sequencing. Mol Cell. 2015;58:610-620
3. Saadatpour A., Lai S., Guo G. and Yuan G. C. Single-Cell Analysis in Cancer Genomics. Trends Genet. 2015;31:576-586

1. Fan H. C., Fu G. K. and Fodor S. P. (2015) Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347: 1258367